气候变化领域集成服务门户(CCPortal): High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels

CCPortal

DOI	10.1016/j.atmosenv.2020.117877
	High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels
	Kang Y.; Li Z.; Lv W.; Xu Z.; Zheng W.X.; Chang J.
发表日期	2021
ISSN	13522310
卷号	244
英文摘要	On-road emission remote sensing (OERS) is an ideal means to identify the on-road high-emitting vehicles, which can scan thousands of vehicles within a day without interfering the normal driving. Due to the complex and varying measuring environments and vehicular operating states, it is reasonable to determine the high-emitters not only by the OERS-output pollutant concentration, but also the other information, such as meteorological and vehicular conditions. This paper aims to establish a high-emitter identification model by machine learning technologies to combine the OERS outputs and periodic emission inspection results. The periodic emission inspection, which is conducted in vehicular inspection stations (VIS), is relatively accurate since the measuring environments and vehicular operating states are controllable, and thereby the periodic emission inspection results are considered as the truth values (or labels). However, VIS is extremely inefficient compared with OERS, thus resulting in scarce labels. Moreover, due to some practical issues, such as staff cheating, only the positive labels (high-emitters) are reliable. Therefore, this paper studies the possibility of employing the one-class classification and graph-based label propagation to solve the problem of scarce positive labels. The experimental results show that the high-emitter identification model based on one-class classification can achieve satisfactory performance, which could be further improved by the application of graph-based label propagation. © 2020 Elsevier Ltd
英文关键词	High-emitter identification; Label propagation; One-class classification; Scarce positive labels
语种	英语
scopus关键词	Graphic methods; Inspection; Road vehicles; Roads and streets; Emitter identification; High emitting vehicles; Label propagation; Machine learning technology; On-road emissions; One-class Classification; Pollutant concentration; Practical issues; Remote sensing; concentration (composition); detection method; experimental study; identification method; machine learning; operations technology; performance assessment; remote sensing; traffic emission; article; human; machine learning; remote sensing
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/144882
作者单位	Department of Automation, University of Science and Technology of China, Hefei, 230027, China; Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230027, China; State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, 230027, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China; School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW 2751, Australia
推荐引用方式 GB/T 7714	Kang Y.,Li Z.,Lv W.,et al. High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels[J],2021,244.
APA	Kang Y.,Li Z.,Lv W.,Xu Z.,Zheng W.X.,&Chang J..(2021).High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels.Atmospheric Environment,244.
MLA	Kang Y.,et al."High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels".Atmospheric Environment 244(2021).